Computing the Orientation of Hardware Components from Images Using Traditional Computer Vision Methods

• Published in: Engineering proceedings Vol. 65; no. 1; p. 8
• Main Authors: Antonio Gambale, Emmett Kerr, Dermot Kerr, Sonya Coleman
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-03-2024
• Subjects: machine learning; manufacturing; PCA; vision
• ISSN: 2673-4591
• DOI: 10.3390/engproc2024065008

This paper introduces a methodology for precise object orientation determination using principal component analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with axis-aligned bounding boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing oriented bounding boxes. the methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.